# se.coef: Extract Standard Errors of Model Coefficients In arm: Data Analysis Using Regression and Multilevel/Hierarchical Models

 se.coef R Documentation

## Extract Standard Errors of Model Coefficients

### Description

These functions extract standard errors of model coefficients from objects returned by modeling functions.

### Usage

```se.coef (object, ...)
se.fixef (object)
se.ranef (object)

## S4 method for signature 'lm'
se.coef(object)
## S4 method for signature 'glm'
se.coef(object)
## S4 method for signature 'merMod'
se.coef(object)
```

### Arguments

 `object` object of `lm`, `glm` and `merMod` fit `...` other arguments

### Details

`se.coef` extracts standard errors from objects returned by modeling functions. `se.fixef` extracts standard errors of the fixed effects from objects returned by lmer and glmer functions. `se.ranef` extracts standard errors of the random effects from objects returned by lmer and glmer functions.

### Value

`se.coef` gives lists of standard errors for `coef`, `se.fixef` gives a vector of standard errors for `fixef` and `se.ranef` gives a list of standard errors for `ranef`.

### Author(s)

Andrew Gelman gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn

### References

Andrew Gelman and Jennifer Hill. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

`display`, `coef`, `sigma.hat`,

### Examples

```# Here's a simple example of a model of the form, y = a + bx + error,
# with 10 observations in each of 10 groups, and with both the
# intercept and the slope varying by group.  First we set up the model and data.

group <- rep(1:10, rep(10,10))
mu.a <- 0
sigma.a <- 2
mu.b <- 3
sigma.b <- 4
rho <- 0
Sigma.ab <- array (c(sigma.a^2, rho*sigma.a*sigma.b,
rho*sigma.a*sigma.b, sigma.b^2), c(2,2))
sigma.y <- 1
ab <- mvrnorm (10, c(mu.a,mu.b), Sigma.ab)
a <- ab[,1]
b <- ab[,2]
#
x <- rnorm (100)
y1 <- rnorm (100, a[group] + b[group]*x, sigma.y)
y2 <- rbinom(100, 1, prob=invlogit(a[group] + b*x))

#  lm fit
M1 <- lm (y1 ~ x)
se.coef (M1)

#  glm fit
M2 <- glm (y2 ~ x)
se.coef (M2)

#  lmer fit
M3 <- lmer (y1 ~ x + (1 + x |group))
se.coef (M3)
se.fixef (M3)
se.ranef (M3)

#  glmer fit
M4 <- glmer (y2 ~ 1 + (0 + x |group), family=binomial(link="logit"))
se.coef (M4)
se.fixef (M4)
se.ranef (M4)
```

arm documentation built on Aug. 29, 2022, 1:05 a.m.